The mediating role of financial market development in the relationship between public governance and logistics performance index in Asian countries

Duc Anh Nguyen - Thi Tuyet Nhi Nguyen - Quynh Anh Nguyen - Khanh Linh Tran - Viet Ha Nguyen Dac - Thi Huong Tran (School of Economics and Management, Hanoi University of Science and Technology)


Logistics is considered the lifeblood of the economy. Currently, there are quite a few indicators to evaluate the competitiveness and performance of the logistics service industry in each country, but the logistics performance index (Logistics Performance Index - LPI) developed by the World Bank is the most widely recognized and applied. Finding out the factors that directly affect or mediate variables in the relationship between influencing factors and LPI is a topic of interest to both government, business, and researchers. This study aims to investigate the direct impact of financial market development factors on the logistics performance index and to discover whether financial market development meditates the relationship between public governance and the logistics performance index in Asian countries. The study applied a structural equation modeling with data of 40 countries in Asia from three sources: the World Bank, the Worldwide Governance Indicators and the World Economic Forum. The findings indicated that the factors of financial market development and public governance are significantly associated with the logistics performance index, and the financial market plays a pivotal part as an important mediator in this relationship.

Keywords: financial market development, influencing factor, mediating role, public governance, logistics performance index (LPI), Asia.

1. Introduction

Logistics has been considered as a critical sector, and has a fundamental role in global supply chain management. From the macro perspective, logistics has a significant contribution to the nation’s economic growth. As efficient logistics systems enable businesses to operate more efficiently, reducing costs, and increasing competitiveness, which could attract investment, create jobs, and stimulate economic growth. Moreover, trade competitiveness of a country is highly affected by logistics systems because powerful logistics structures benefit countries in global trade by transporting goods more quickly and cost-effectively, leading to increased export revenues, foreign investment, and economic development. To evaluate the effectiveness of logistics systems of countries, in 2007, the World Bank developed the Logistics Performance Index (LPI). LPI is widely accepted, recognized, and applied by governments, business, and researchers among other logistics criteria. The LPI of World Bank is calculated basing on a survey of global freight forwarders and logistics experts who provide their perceptions of a country's logistics infrastructure, services, and the overall efficiency of supply chain operations. According to the World Bank, there are 6 core components of LPI: customs, infrastructure, international shipments, quality/ competence of logistics services, tracking & tracing, and timeliness (World Bank, 2023). The first component, customs, refers to “the efficiency of customs and border management clearance” (for example speed and simplicity). Secondly, infrastructure refers to the quality of infrastructure for trade and transport. Thirdly, international shipments reflect the ease level when arranging for shipments with competitive prices.  Fourthly, quality of logistics services refers to “the competence and quality of logistics services—trucking, forwarding, and customs brokerage”. Fifthly, tracking & tracing refers to “the ability to track and trace shipments”. Finally, timeliness refers to “the frequency with which shipments reach consignees within scheduled or expected delivery times”. (World Bank, 2023)

Numerous studies investigated the influence of LPI on the development and sustainability of nations and firms as well as factors influencing LPI (Bîzoi & Sipos, 2014; Green et al., 2008;

Kabak et al., 2020; Liu et al., 2018; Wong & Tang, 2018). Guner & Coskun (2012) compared the impacts of economic and social factors on LPI of 26 OECD countries. Green et al. (2008) also pointed out that logistics performance positively impacts marketing, leading to the development of financial performance in an organization.  Song & Lee (2022) highlighted the relationship between international trade and logistics performance in Korea. Among many influencing factors, public governance ( for example, laws and regulations, degree of corruption, and political stability) is considered as one of the most significant factors having impacts on all six dimensions of LPI (Guner & Coskun, 2012; Wong & Tang, 2018; Uyar et al., 2021). However,  the association between the two variables (LPI and public governance) is not always stable but wobbly by different mediators. Understanding the mediator between the two variables,  policymakers, investors, and logistics operators would have more effective decisions to increase performance of logistics systems. Uyar et al. (2021) conducted an empirical study about mediation mechanism of corporate government on the relationship between public governance and LPI. Özdemir (2017) indicated that financial development and logistics performance have an association and together contribute positively to the competitiveness of countries.  There is a lack of studies about relationship between public governance, financial market, and LPI, as well as demonstrating the mediating roles of financial market development in the relationship between public governance and LPI, especially, in the context of Asia countries. This paper will use secondary data collected from World Bank, Worldwide Governance Indicators, and World Economic Forum and Structural Equation Model (SEM) to fill the research gap.

2. Theoretical background and research model

This research is based on two main theoretical backgrounds: Institutional theory and finance and growth theory. The institutional theory reckons that “organizations implement business practices because doing so enhances their legitimacy” (DiMaggio & Powell, 1983)financial market development and suggests that the shape of industrial clusters is affected by not only the international economy but also social governance (Gereffi & Lee, 2016). Dzhumashev, (2014) found that countries with “good governance” are assumed to determine economic efficiency. According to Jhawar et al. (2017), nations that have good public governance quality are likely to contribute to logistics performance by improving regulations and establishing essential institutional reforms.

According to finance and growth theory, “better developed financial systems ease external financing constraints facing firms, which illuminates one mechanism through which financial development influences economic growth”. Lean et al., (2014) investigated the impact of financial mechanisms on logistics by testing the link between the growth of logistics and economic expansion. Financial market development can boost the growth of economies and then economic growth increases demand for logistics services, which contributes to the development of the logistics industry. Thus, this study postulates that financial development is crucial for firms to improve financial mechanisms, resulting in higher performance for the whole logistics system.  According to Kabak et al., (2020) only after Business Sophistication, the second pillar that should be prioritized to enhance a country's logistics performance is the financial market development. Financial development allows logisticians to have access to a variety of financial products and services to finance capital assets, working capital, and inventory; to insure or help hedge various risks/uncertainties; and to facilitate the interchange of goods, services, and information (Özdemir, 2017).

According to McKinsey, (2021), Asia is projected to be the region that recover fastest globally after Covid-19 and soon becomes the hub of all logistics activities of all other regions in the world. While China, India, and Japan are the largest logistics markets in Asia, Indonesia, Vietnam, and Thailand have the highest growth potential. Some other Asian countries such as Myanmar, Pakistan, Mongolia, Bangladesh, and Nepal were assessed as having quite low-score of LPI. Therefore, it is necessary to have intensive research focusing on the Asia region.

Our research proposes four hypotheses depicted in Figure 1 in the Asia context.

H1: Public governance has a positive significant association with logistics performance.

H2: Financial market has a positive significant association with logistics performance

H3: Public governance has a positive significant association with the financial market.

H4: Financial market mediates the association between public governance and logistics performance.

Figure 1: Research model

financial market


3. Research Methodology

3.1 Data collection

The research model includes three components: LPI, public governance, and financial market development. In the first place, LPI was proxied using the six main indicators, and the data was acquired from the World Bank (2018): LPI1 - Efficiency of the clearing process, LPI2 - Quality of trade and transport related to infrastructure, LPI3 - Ease of arranging competitively priced shipments, LPI4 - Competence and quality of logistics services, LPI5 - Ability to track and trace consignments, LPI6 - Timeliness of shipments. Secondly, public governance was proxied by the four indicators which were retrieved from the Worldwide Governance Indicators (2018): PGOV1 – Political stability and absence of violence/terrorism, PGOV2 - Regulatory quality, PGOV3 - Rule of law, PGOV4 - Control of corruption. These indicators employ a comprehensive approach to capture several facets of the effectiveness of public governance and were used by prior studies as a proxy for public governance quality (Chong et al., 2020; Mahmood et al., 2021). Lastly, financial market was proxied by the following five indicators collected from the World Economics Forum (2018), FM1 - Extent and effect of taxation, FM2 - Availability of financial services, FM3 – Ease of access to loans, FM4 - Soundness of bank, FM5 - Venture capital availability.

Authors gather data from three sources: the World Bank, the Worldwide Governance Indicators, and the World Economic Forum in the year of 2007, 2010, 2012, 2014, 2016, and 2018. Initially, the dataset contained records for 51 countries in 6 years, which could be considered as 306 records (51 x 6 = 306). However, there were 5 nations eliminated due to missing more than 3 country-year records. This resulted in 276 records. Finally, there are 10 countries (of financial market component) and 1 country (of public governance component) had more than 3 country-year missing values, which were also eliminated. Therefore, the final sample size was 210 records (276 - 11x6 = 210). To synchronize scales of three components, scales of indicators in public governance were converted from the original -2.5 - 2.5 scale while scales of financial market were converted from the original 1 - 7 scale.

3.2 Data analysis

Authors used Structural Equation Modeling (SEM) to analyze data and test the relationship between public governance, financial market, and logistics performance. SEM is a widely used technique in the statistical analysis developed to analyze multidimensional relationships between variables in a model (Byrne, 2013). SEM allows answering questions related to regression analysis of factors, research and analysis of influencing factors or complex relationships. Before analyzing data with SEM, we used Principal Component Analysis to extract factors and Confirmatory Factor Analysis (CFA) to confirm factor structures and to evaluate reliability and validity, as well as the fit of the model.

4. Result

4.1 Descriptive Statistics

Table 1. Descriptive Statistics (N = 210) 





















S.D.: Standard Deviation.

Source: Summarized and calculated by authors

Table 1 provides a descriptive analysis of LPI, PGOV, FM. The average LPI value (2.98), demonstrates that the global logistics industry can operate more effectively. Singapore, Japan, and Hong Kong SAR are among the highest-ranked nations based on the average of the six LPI indicators. All three countries have high LPI mainly resulting from following reasons: efficient custom procedures, supportive business environment, and strong government support. While Nepal, Mongolia, and Tajikistan are among the countries with the lowest-ranked performances because of some reasons such as inadequate infrastructure, limited logistics services, and political instability. Lebanon, Yemen, and Armenia are the three nations where LPI scores are highly erratic. The factors that cause the LPI in these countries to fluctuate at such a high level are political instability, economic challenges, and geopolitical factors. To be more specific, in 2012, Lebanon faced the ongoing conflict with Syria contributed to increased security concerns, reduced trade volumes, leading to increase transportation costs. Yemen has been in a state of civil since 2014, which had resulted in the fragmentation of the country and the collapse of government institutions. Therefore, Yemen suffered from delays and corruption at ports and borders, making goods import/export more difficult and expensive.

4.2 Exploratory Factor Analysis (EFA)

According to exploratory analysis result, the cumulative proportion of variance explained by the extracted factors was 85.5%, which is significantly higher than the cut-off value of 60% (Hinkin, 1998). Additionally, the Bartlett's Test of Sphericity test statistics revealed that the variables of interest sufficiently relate to each other to execute the EFA (Bartlett's Test of Sphericity = 4411.151, df = 105, p-value = 0.000) and the Kaiser-Meyer-Olkin (KMO) test statistics demonstrated that the data is adequate for the EFA (KMO = 0.942). Furthermore, Cronbach’s Alpha index ranged from 0.917 to 0.979, which assumed that the set of survey items is highly reliable. Since the items within each extracted LPI, PGOV, and FM component are significantly correlated, convergent validity is satisfied.

4.3 Confirmatory Factor Analysis (CFA)

Results from Confirmatory Factor Analysis showed that Chi-square/df was 2.272, the Comparative Fit Index (CFI) was 0.976, the Tucker-Lewis Index (TLI) was 0.970, and the Root Mean Square Error of Approximation (RMSEA) was 0.078 which satisfied the advised cut-off levels. The Goodness of Fit Index (GFI) (0.889) hardly satisfies the cut-off levels (0.9) according to the limitation of the sample size. However, the factor-structure models of Doll et al. (1994) accepted that 0.8 is the cut-off value of GFI; thus, the GFI in our study still satisfied the threshold.

4.4 Structured Equation Model (SEM)

Table 2 shows that H1 which investigates the connection between public governance and logistics performance index, is supported by a standardized regression weight of 0.587, CR = 7.925, and p < 0.01. H2 which investigates the relationship between financial markets and the logistics performance index, is supported by standardized regression weight = 0.279, CR = 3.657, and p < 0.01. The empirical data suggested that PGOV and FM both have positive impact on the LPI. Subsequently, we examined the connection between public governance and financial market. With standardized regression weight = 0.779, CR = 11.512, and p < 0.01, we suggest that, at a 1% level of significance, PGOV has a highly significant positive association with FM. Thus, all three hypotheses H1, H2, and H3 are supported.

Additionally, as the footnote of the table 2, 60.7% of the variance in FM is clarified by the variance in PGOV, and 67.6% of the variance in LPI is clarified by the variance in PGOV and FM. This allows us to think about the mediator relationship between these variables.

Table 2. Structured Equation Model Result








-->           LPI






-->           LPI






-->           FM




***p < 0.01; R2FM = .607; R2LPI = .676

Source: Analysis results of authors

4.5 Mediation Analysis

The mediation analysis results provide the connection between PUBG and LPI through CORPG. PROCESS macro (Hayes, 2013) was used to test the association between PUBG and LPI through CORPG. The application of bias-corrected bootstrapping methodology was deemed appropriate because it is a methodology that does not rely on assumptions, and at the same time, it controls Type-1 errors. By utilizing the bias-corrected bootstrapping method, the bias-corrected confidence intervals for both the direct and indirect effects were calculated at a 95% level of confidence, with 1,000 bootstraps resampling. If the confidence interval in the mediation analysis does not include zero, then the obtained results are deemed to be statistically significant.

According to the findings of the mediation analysis, zero is not included in the lower or higher confidence interval boundaries, and the indirect impact (0.217) is statistically significant. Therefore, CORPG mediates the relationship between PUBG and LPI. Furthermore, we need to look at the direct effect to see if the mediation is full or partial. If the direct effect is statistically insignificant, the mediation is deemed to be full; if the direct effect is statistically significant, the mediation is deemed to be partial. Therefore, CORPG partially mediates the link between PUBG and LPI. In conclusion, H4 is supported.

Table 3. Mediation Analysis Result


Direct Effect

Indirect Effect


H4: PGOV --> FM --> LPI


LLCI = 0.407; ULCI = 0.750


LLCI = 0.088; ULCI = 0.374

Partial mediation

***p < 0.01; LLCI: Lower Limit Confidence Interval; ULCI: Upper Limit Confidence Interval.

Source: Analysis results of authors

5. Conclusion

In this study, our findings disclose that public governance quality is significantly associated with logistics performance, and the financial market plays a crucial part as a significant mediator in this relationship as well as has impact on the logistics performance. Research results suggest policymakers in Asia to focus on developing both financial markets and effective public governance frameworks to improve logistics performance. It can be achieved through the implementation of policies and regulations that promote financial market development and effective public governance, while also encouraging private sector participation and investment in the logistics sector. Although our results reveal a holistic relationship between public governance, financial market, and logistics performance, they do not highlight which aspects of public governance influence logistics performance more than the others and similar limitations are witnessed in financial market indicators. Thus, future research could overcome these constraints to provide more detailed suggestions by focusing on individual indicators of public governance and financial market development.


  1. Bîzoi, A.-C., & Sipos, C. (2014). Logistics Performance and Economic Development -A comparison within the European Union. Multidisciplinary academic conference on economics, management and marketing in Prague, MAC-EMM 2014. At: Prague, Czech RepublicVolume: Proceedings of MAC-EMM 2014. DOI:10.13140/2.1.1789.2163
  2. Byrne, B. M. (2013). Structural Equation Modeling with Mplus. New York: Routledge.
  3. Chong, S. P. C., Tee, C. M., & Cheng, S. V. (2020). Political institutions and the control of corruption: A cross-country evidence. Journal of Financial Crime, 28(1), 26-48.
  4. DiMaggio, P. J., & Powell, W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American Sociological Review, 48(2), 147.
  5. Dzhumashev, R. (2014). Corruption and growth: The role of governance, public spending, and economic development. Economic Modelling, 37, 202-215.
  6. Gereffi, G., & Lee, J. (2016). Economic and Social Upgrading in Global Value Chains and Industrial Clusters: Why Governance Matters. Journal of Business Ethics, 133(1), 25-38.
  7. Green, K. W., Whitten, D., & Inman, R. A. (2008). The impact of logistics performance on organizational performance in a supply chain context. Supply Chain Management: An International Journal, 13(4), 317-327.
  8. Guner, S., & Coskun, E. (2012). Comparison of impacts of economic and social factors on countries’ logistics performances: A study with 26 OECD countries. Research in Logistics & Production, 2(4), 329-343.
  9. Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York: Guilford Press.
  10. Jhawar, A., Garg, S. K., & Khera, S. N. (2017). Improving logistics performance through investments and policy intervention: A causal loop model. International Journal of Productivity and Quality Management, 20(3), 363.
  11. Kabak, Ö., Ekici, Ş. Ö., & Ülengin, F. (2020). Analyzing two-way interaction between the competitiveness and logistics performance of countries. Transport Policy, 98, 238-246.
  12. Lean, H. H., Huang, W., & Hong, J. (2014). Logistics and economic development: Experience from China. Transport Policy, 32, 96-104.
  13. Liu, J., Yuan, C., Hafeez, M., & Yuan, Q. (2018). The relationship between environment and logistics performance: Evidence from Asian countries. Journal of Cleaner Production, 204, 282–291.
  14. Mahmood, H., Tanveer, M., & Furqan, M. (2021). Rule of Law, Corruption Control, Governance, and Economic Growth in Managing Renewable and Nonrenewable Energy Consumption in South Asia. International Journal of Environmental Research and Public Health, 18(20), 10637.
  15. (2021). Asia: The highway of value for global logistics. Available at:
  16. Özdemir, L. (2017). Relationship between financial development and logistics performance and their effects on the competitiveness: An empirical cross-country. PhD thesis, Middle East Technical University.
  17. Uyar, A., Fernandes, V., & Kuzey, C. (2021). The mediating role of corporate governance between public governance and logistics performance: International evidence. Transport Policy, 109, 37-47.
  18. Wong, W. P., & Tang, C. F. (2018). The major determinants of logistic performance in a global perspective: Evidence from panel data analysis. International Journal of Logistics Research and Applications, 21(4), 431-443.




Nguyễn Đức Anh1

Nguyễn Quỳnh Anh1

Trần Khánh Linh1

Nguyễn Thị Tuyết Nhi1

Nguyễn Đắc Việt Hà1

Trần Thị Hương1

1Viện Kinh tế và Quản lý, Đại học Bách khoa Hà Nội

Tóm tắt:

Logistics được coi là mạch máu của nền kinh tế. Hiện nay, có nhiều chỉ số để đánh giá năng lực cạnh tranh và hiệu quả hoạt động của ngành dịch vụ logistics mỗi quốc gia, nhưng chỉ số hiệu quả logistics (Logistics Performance Index-LPI) do Ngân hàng Thế giới xây dựng được công nhận và sử dụng rộng rãi nhất. Tìm ra được các nhân tố ảnh hưởng trực tiếp hay các biến trung gian trong mối quan hệ của các nhân tố đối với chỉ số LPI là chủ đề được sự quan tâm của cả chính phủ, doanh nghiệp, và các nhà nghiên cứu. Nghiên cứu này nhằm mục đích xác định mối liên hệ giữa sự phát triển thị trường tài chính đến chỉ số hiệu quả logistics và khám phá xem liệu sự phát triển thị trường tài chính có điều chỉnh mối quan hệ giữa quản trị công và chỉ số hiệu quả logistics ở các nước châu Á hay không? Nhóm nghiên cứu đã áp dụng mô hình phương trình cấu trúc (Structural Equation Modeling - SEM) với dữ liệu của 40 quốc gia ở Châu Á lấy từ 3 nguồn, gồm: Ngân hàng Thế giới (World Bank), Chỉ số Quản trị toàn cầu (Worldwide Governance Indicators) và Diễn đàn Kinh tế Thế giới (World Economic Forum). Các phát hiện chỉ ra rằng, sự phát triển thị trường tài chính và quản trị công có liên quan đáng kể đến chỉ số hiệu quả logistics. Bên cạnh đó, sự phát triển của thị trường tài chính cũng đóng vai trò như một biến trung gian quan trọng trong mối quan hệ giữa quản trị công và chỉ số hiệu quả logistics.

Từ khóa: chỉ số hiệu quả logistics, nhân tố ảnh hưởng, biến trung gian, quản trị công, sự phát triển thị trường tài chính, các nước châu Á.

[Tạp chí Công Thương - Các kết quả nghiên cứu khoa học và ứng dụng công nghệ, Số 8 tháng 4 năm 2023]

Tạp chí Công Thương